Modified maximum likelihood estimation of Gaussian moving averages using a pseudoquadratic convergence criterion

Biometrika ◽  
1978 ◽  
Vol 65 (1) ◽  
pp. 203-206 ◽  
Author(s):  
E. J. GODOLPHIN
2016 ◽  
Vol 853 ◽  
pp. 458-462
Author(s):  
Hai Yan Xing ◽  
Hua Ge ◽  
Guan Ga Dai ◽  
Zheng Shuai Yu ◽  
Xiao Jun Sun

In order to quantitatively identify critical hidden damage for weld joints by using the metal magnetic memory technology (MMM), the modified maximum likelihood estimation MMM model is first proposed. The experimental materials are Q235B welded plate specimens. Fatigue tension experiments were operated to find the MMM feature laws of critical hidden damage by comparing with synchronous X-ray detection results. Four MMM characteristic parameters, that is, ΔHp(y) , Kymax , mmax and S(K), are extracted corresponding to the normal state and the hidden damage state, respectively. The probability density values of ΔHp(y) , Kymax , mmax and S(K)are calculated by the optimized bandwidth kernel density estimation. The quantitative maximum likelihood estimation MMM model is established based on optimized bandwidth kernel density. The verification result shows the maximum likelihood value of hidden damage state is twice as much as that of the normal state, which is consistent with the practical results. This provides a new method for quantitative MMM identification of weld critical hidden damages.


Author(s):  
Mustapha Muhammad ◽  
Isyaku Muhammad ◽  
Aisha Muhammad Yaya

In this paper, a new lifetime model called Kumaraswamy exponentiated U-quadratic (KwEUq) distribution is proposed. Several mathematical and statistical properties are derived and studied such as the explicit form of the quantile function, moments, moment generating function, order statistics, probability weighted moments, Shannon entropy and Renyi entropy. We also found that the usual maximum likelihood estimates (MLEs) fail to hold for the KwEUq distribution. Two alternative methods are suggested for the parameter estimation of the KwEUq, the alternative maximum likelihood estimation (AMLE) and modified maximum likelihood estimation (MMLE). Simulation studies were conducted to assess the finite sample behavior of the AMLEs and MMLEs. Finally, we provide application of the KwEUq for illustration purposes.


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